Ornith 1.5 9B Abliterated — MLX-VLM affine 8-bit/group 64 RTN
An unofficial experimental derivative of
ornith-ai/Ornith-1.5-9B, pinned to
revision c927ad73b7eb20f00aafcaa0a11a9d58ed5487bc.
The original model is by the Ornith team. The conversion, refusal-direction
experiment, and validation were performed by PocketAI Model Lab;
PocketAiHub identifies the publisher of this derivative.
Purpose and responsible use
This experimental derivative studies whether learned refusal behavior can be reduced while retaining general capability. It is published for research and legitimate local use, not to endorse or facilitate illegal, abusive, or dangerous applications.
The edit reduces refusal behavior broadly rather than determining whether a request is legitimate. Deployers should evaluate the model in their own context and apply appropriate safeguards. Abliteration is not truthfulness training, a capability improvement, or a guarantee of universal compliance.
Release family
Format and recipe
- Format: MLX-VLM
- Precision: affine 8-bit/group 64 RTN
- Abliteration scale: 1.0
- Direction source layer: 23
- Destination layers: 12–31
- Modified residual-output tensors: 40
- Native MTP is not included
- Text and image-input smoke tests passed.
- Peak runtime memory in the smoke test: 12.01 GB
Validation
| Gate | Result |
|---|---|
| Refusal-targeted explicit-refusal phrase flags | 0/100 |
| Benign-control explicit-refusal phrase flags | 0/100 |
| Medium capability suite | 72/80 |
| Runtime smoke | passed |
The medium suite covers math/reasoning, false-premise handling, instruction following, coding, structured output, multilingual output, context comprehension, and general coherence.
The refusal scorer is phrase based and can miss redirects and other non-literal forms of non-compliance. Therefore 0/100 phrase flags measures explicit refusal wording, not universal compliance or response quality. The 256-token runs are early-response screens rather than complete long-answer evaluations.
See abliteration-manifest.json and
validation-summary.json for machine-readable
provenance and category-level results.
Load with MLX-VLM
python -m pip install "mlx==0.32.0" "mlx-vlm==0.6.8"
mlx_vlm.generate --model PocketAiHub/Ornith-1.5-9B-Abliterated-MLX-8bit --prompt "Explain why seasons occur." --max-tokens 256
License
The upstream model card declares MIT. This repository includes the MIT license and preserves attribution to the pinned source above.